Neural-Network Classifiers for Recognizing Totally Unconstrained Handwritten Numerals  

Oleh:

Cho, Sung-Bae

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 8 no. 1 (1997), page 43-53.
Topik: neural network; neural - network; recognizing; handwritten numerals

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Artificial neural networks have been recognized as a powerful tool for pattern classification problems, but a number of researchers have also suggested that straightforward neural - network approaches to pattern recognition are largely inadequate for difficult problems such as handwritten numeral recognition. In this paper, we present three sophisticated neural - network classifiers to solve complex pattern recognition problems : multiple multilayer perceptron (MLP) classifier, hidden Markov model (HMM) / MLP hybrid classifier, and structure - adaptive self - organizing map (SOM) classifier. In order to verify the superiority of the proposed classifiers, experiments were performed with the unconstrained handwritten numeral database of Concordia University, Montreal, Canada. The three methods have produced 97.35 %, 96.55 %, and 96.05 % of the recognition rates, respectively, which are better than those of several previous methods reported in the literature on the same database.

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